Introduction

In this module, we will discuss the following concepts:

1. Explore Landsat 7 and Landsat 8 collection.
2. How to perform cloud masking and cloud masking assessments in Google Earth Engine for Landsat 8 surface reflectance imagery.
3. How to generate cloud-free image composites and how to filter out the SLC-off issue.

Acknowledgment: This assignment is adapted and expanded from a lab assignment by Dan Carver and Peder Engelstad, Ecology and Natural Resource Ecology Laboratory at CSU. 

Background

What is Pre-Processing?
Much of the data you will find in Google Earth Engine (GEE) will have some level of pre-processing. This involves several different methods of quality control to ensure the highest levels of accuracy and consistency within raster collections. Depending on the collection, there may be a variety of pre-processing levels available and it is important to understand the differences to successfully use remotely sensed data. Three common sources of error for imagery products are consistently addressed by publishers before data are made available in GEE: atmosphere (i.e. air chemistry), topography (i.e. elevation), and geometry (i.e. pixel consistency).

In a previous assignment, we compared the top of atmosphere and surface reflectance data. The surface reflectance data have received the highest level of pre-processing in an attempt to best represent the actual conditions on the ground where a certain amount of solar energy bounces back (is reflected) toward aerial and spaceborne sensors. However, even surface reflectance products can be adversely affected by low sun angle, excessive clouds, and coverage locations over latitudes greater than 65 degrees north or south. Despite this, it is recommended that analyses over multiple dates (e.g. change detection) or large geographic scale (e.g. algorithmic prediction) use Landsat surface reflectance data. 

As a reminder, we will start loading a TOA and Surface Reflectance composite over Oklahoma.